


import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

A   =			(
    "4S 3H 2C 7S 5H",
    "9D 8H 2C 6S 7H",
    "2D 6D 9D TH 7D",
    "TC 8C 2S JH 6C",
    "JH 8S TH AH QH",
    "TS KS 5S 9S AC",
    "KD 6S 9D TH AD",
    "KS 8D 4D 9S 4S",  # pair
    "8C 4S KH JS 4D",  # pair
    "QH 8H KD JH 8S",  # pair
    "KC 4H KS 2H 8D",  # pair
    "KD 4S KC 3H 8S",  # pair
    "AH 8S AS KC JH",  # pair
    "3H 4C 4H 3S 2H",  # 2 pairs
    "5S 5D 2C KH KH",  # 2 pairs
    "3C KH 5D 5S KH",  # 2 pairs
    "AS 3C KH AD KH",  # 2 pairs
    "7C 7S 3S 7H 5S",  # 3 of a kind
    "7C 7S KH 2H 7H",  # 3 of a kind
    "AC KH QH AH AS",  # 3 of a kind
    "2H 4D 3C AS 5S",  # straight (low ace)
    "3C 5C 4C 2C 6H",  # straight
    "6S 8S 7S 5H 9H",  # straight
    "JS QS 9H TS KH",  # straight
    "QC KH TS JS AH",  # straight (high ace)
    "8C 9C 5C 3C TC",  # flush
    "3S 8S 9S 5S KS",  # flush
    "4C 5C 9C 8C KC",  # flush
    "JH 8H AH KH QH",  # flush
    "3D 2H 3H 2C 2D",  # full house
    "2H 2C 3S 3H 3D",  # full house
    "KH KC 3S 3H 3D",  # full house
    "JC 6H JS JD JH",  # 4 of a kind
    "JC 7H JS JD JH",  # 4 of a kind
    "JC KH JS JD JH",  # 4 of a kind
    "2S AS 4S 5S 3S",  # straight flush (low ace)
    "2D 6D 3D 4D 5D",  # straight flush
    "5C 6C 3C 7C 4C",  # straight flush
    "JH 9H TH KH QH",  # straight flush
    "JH AH TH KH QH",  # royal flush (high ace straight flush)
)

A   =			(
    ("2H 3H 4H 5H 6H", "KS AS TS QS JS", "Loss"),
    ("2H 3H 4H 5H 6H", "AS AD AC AH JD", "Win"),
    ("AS AH 2H AD AC", "JS JD JC JH 3D", "Win"),
    ("2S AH 2H AS AC", "JS JD JC JH AD", "Loss"),
    ("2S AH 2H AS AC", "2H 3H 5H 6H 7H", "Win"),
    ("AS 3S 4S 8S 2S", "2H 3H 5H 6H 7H", "Win"),
    ("2H 3H 5H 6H 7H", "2S 3H 4H 5S 6C", "Win"),
    ("2S 3H 4H 5S 6C", "3D 4C 5H 6H 2S", "Tie"),
    ("2S 3H 4H 5S 6C", "AH AC 5H 6H AS", "Win"),
    ("2S 2H 4H 5S 4C", "AH AC 5H 6H AS", "Loss"),
    ("2S 2H 4H 5S 4C", "AH AC 5H 6H 7S", "Win"),
    ("6S AD 7H 4S AS", "AH AC 5H 6H 7S", "Loss"),
    ("2S AH 4H 5S KC", "AH AC 5H 6H 7S", "Loss"),
    ("2S 3H 6H 7S 9C", "7H 3C TH 6H 9S", "Loss"),
    ("4S 5H 6H TS AC", "3S 5H 6H TS AC", "Win"),
    ("2S AH 4H 5S 6C", "AD 4C 5H 6H 2C", "Tie"),
    ("AS AH 3H AD AC", "AS AH 2H AD AC", "Win"),
    ("AH AC 5H 5C QS", "AH AC 5H 5C KS", "Loss"),
    ("AH AC 5H 5C QS", "KH KC 5H 5C QS", "Win"),
    ("7C 7S KH 2H 7H", "3C 3S AH 2H 3H", "Win"),
    ("3C 3S AH 2H 3H", "7C 7S KH 2H 7H", "Loss"),
    ("6H 5H 4H 3H 2H", "5H 4H 3H 2H AH", "Win"),
    ("5H 4H 3H 2H AH", "5H 4H 3H 2H AH", "Tie"),
    ("5H 4H 3H 2H AH", "6H 5H 4H 3H 2H", "Loss"),
    ("AH AD KS KC AC", "AH KD KH AC KC", "Win"),
    ("2H 4D 3C AS 5S", "2H 4D 3C 6S 5S", "Loss"),
    ("2H 3S 3C 3H 2S", "3S 3C 2S 2H 2D", "Win"),
    ("4D 6D 5D 2D JH", "3S 8S 3H TC KH", "Loss"),
    ("4S 6C 8S 3S 7S", "AD KS 2D 7D 7C", "Loss"),
    ("6S 4C 7H 8C 3H", "5H JC AH 9D 9C", "Loss"),
    ("9D 9H JH TC QH", "3C 2S JS 5C 7H", "Win"),
    ("2H TC 8S AD 9S", "4H TS 7H 2C 5C", "Win"),
    ("9D 3S 2C 7S 7C", "JC TD 3C TC 9H", "Loss"),
)

A   =			(
    ("2H 3H 4H 5H 6H", True),
    ("AS AH 2H AD AC", False),
    ("2H 3H 5H 6H 7H", True),
    ("KS AS TS QS JS", True),
    ("8H 9H QS JS TH", False),
    ("AS 3S 4S 8S 2S", True),
)

A   =			(
    ("2H 3H 4H 5H 6H", True),
    ("AS AH 2H AD AC", False),
    ("2H 3H 5H 6H 7H", False),
    ("KS AS TS QS JS", True),
    ("8H 9H QS JS TH", True),
)

A   =			(
    ("2H 4D 3C AS 5S", True, [5, 4, 3, 2, 14]),
    ("2H 5D 3C AS 5S", False, [14, 5, 5, 3, 2]),
    ("JH QD KC AS TS", False, [14, 13, 12, 11, 10]),
    ("9D 3S 2C 7S 7C", False, [9, 7, 7, 3, 2]),
)

A   =			(
    ("JH AH TH KH QH", 0),
    ("JH 9H TH KH QH", 0),
    ("JC KH JS JD JH", 7),
    ("KH KC 3S 3H 3D", 6),
    ("8C 9C 5C 3C TC", 0),
    ("JS QS 9H TS KH", 0),
    ("7C 7S KH 2H 7H", 3),
    ("3C KH 5D 5S KH", 2),
    ("QH 8H KD JH 8S", 1),
    ("2D 6D 9D TH 7D", 0),
)

A   =			(
    ("JH AH TH KH QH", 23),
    ("JH 9H TH KH QH", 22),
    ("JC KH JS JD JH", 21),
    ("KH KC 3S 3H 3D", 20),
    ("8C 9C 5C 3C TC", 19),
    ("JS QS 9H TS KH", 18),
    ("7C 7S KH 2H 7H", 17),
    ("3C KH 5D 5S KH", 16),
    ("QH 8H KD JH 8S", 15),
    ("2D 6D 9D TH 7D", 14),
)





def __UpperCAmelCase							(		)    ->       List[str]:



       '''simple docstring'''





       UpperCAmelCase__    ,      UpperCAmelCase__					    =  randrange(len(a__      )      ), randrange(len(a__      )      )
       UpperCAmelCase__					    =  ["Loss", "Tie", "Win"][(play >= oppo) + (play > oppo)]
       UpperCAmelCase__    ,      UpperCAmelCase__					    =  SORTED_HANDS[play], SORTED_HANDS[oppo]
       return hand, other, expected





def __UpperCAmelCase							(		__A = 1_0_0      )    ->       Optional[Any]:



       '''simple docstring'''





       return (generate_random_hand() for _ in range(a__      ))





@pytest.mark.parametrize("hand, expected"			,    a__      )
def __UpperCAmelCase							(		__A			,    __A      )    ->       List[str]:



       '''simple docstring'''





       assert PokerHand(a__      )._is_flush() == expected





@pytest.mark.parametrize("hand, expected"			,    a__      )
def __UpperCAmelCase							(		__A			,    __A      )    ->       Optional[int]:



       '''simple docstring'''





       assert PokerHand(a__      )._is_straight() == expected





@pytest.mark.parametrize("hand, expected, card_values"			,    a__      )
def __UpperCAmelCase							(		__A			,    __A			,    __A      )    ->       List[str]:



       '''simple docstring'''





       UpperCAmelCase__					    =  PokerHand(a__      )
       assert player._is_five_high_straight() == expected
       assert player._card_values == card_values





@pytest.mark.parametrize("hand, expected"			,    a__      )
def __UpperCAmelCase							(		__A			,    __A      )    ->       Union[str, Any]:



       '''simple docstring'''





       assert PokerHand(a__      )._is_same_kind() == expected





@pytest.mark.parametrize("hand, expected"			,    a__      )
def __UpperCAmelCase							(		__A			,    __A      )    ->       Optional[Any]:



       '''simple docstring'''





       assert PokerHand(a__      )._hand_type == expected





@pytest.mark.parametrize("hand, other, expected"			,    a__      )
def __UpperCAmelCase							(		__A			,    __A			,    __A      )    ->       int:



       '''simple docstring'''





       assert PokerHand(a__      ).compare_with(PokerHand(a__      )      ) == expected





@pytest.mark.parametrize("hand, other, expected"			,    generate_random_hands()      )
def __UpperCAmelCase							(		__A			,    __A			,    __A      )    ->       str:



       '''simple docstring'''





       assert PokerHand(a__      ).compare_with(PokerHand(a__      )      ) == expected





def __UpperCAmelCase							(		)    ->       int:



       '''simple docstring'''





       UpperCAmelCase__					    =  [PokerHand(a__      ) for hand in SORTED_HANDS]
       UpperCAmelCase__					    =  poker_hands.copy()
       shuffle(a__      )
       UpperCAmelCase__					    =  chain(sorted(a__      )      )
       for index, hand in enumerate(a__      ):
              assert hand == poker_hands[index]





def __UpperCAmelCase							(		)    ->       Optional[int]:



       '''simple docstring'''





       UpperCAmelCase__					    =  [PokerHand("2D AC 3H 4H 5S"      ), PokerHand("2S 3H 4H 5S 6C"      )]
       pokerhands.sort(reverse=a__      )
       assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"





def __UpperCAmelCase							(		)    ->       int:



       '''simple docstring'''





       UpperCAmelCase__					    =  PokerHand("2C 4S AS 3D 5C"      )
       UpperCAmelCase__					    =  True
       UpperCAmelCase__					    =  [5, 4, 3, 2, 1_4]
       for _ in range(1_0      ):
              assert pokerhand._is_five_high_straight() == expected
              assert pokerhand._card_values == expected_card_values





def __UpperCAmelCase							(		)    ->       List[Any]:



       '''simple docstring'''





       UpperCAmelCase__					    =  0
       UpperCAmelCase__					    =  os.path.abspath(os.path.dirname(a__      )      )
       UpperCAmelCase__					    =  os.path.join(a__			,    "poker_hands.txt"      )
       with open(a__      ) as file_hand:
              for line in file_hand:
                     UpperCAmelCase__					    =  line[:1_4].strip()
                     UpperCAmelCase__					    =  line[1_5:].strip()
                     UpperCAmelCase__    ,      UpperCAmelCase__					    =  PokerHand(a__      ), PokerHand(a__      )
                     UpperCAmelCase__					    =  player.compare_with(a__      )
                     if output == "Win":
                            answer += 1
       assert answer == 3_7_6






import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 					_UpperCAmelCase			(     lowerCAmelCase,			unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					DDIMPipeline
 __A			       =					UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 __A			       =					PipelineTesterMixin.required_optional_params - {
     '''num_images_per_prompt''',
     '''latents''',
     '''callback''',
     '''callback_steps''',
 }
 __A			       =					UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
 __A			       =					False







 def 		__UpperCAmelCase				(		self				:   int)						->	List[str]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase           =    UNetaDModel(
       block_out_channels=(32, 64)							, layers_per_block=2							, sample_size=32							, in_channels=3							, out_channels=3							, down_block_types=("DownBlock2D", "AttnDownBlock2D")							, up_block_types=("AttnUpBlock2D", "UpBlock2D")							, )
   _UpperCamelCase           =    DDIMScheduler()
   _UpperCamelCase           =    {"unet": unet, "scheduler": scheduler}
   return components







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   List[str]							, lowercase_				:   Optional[Any]=0)						->	Optional[Any]:


   """simple docstring"""







   if str(lowercase_).startswith("mps"):
     _UpperCamelCase           =    torch.manual_seed(lowercase_)
   else:
     _UpperCamelCase           =    torch.Generator(device=lowercase_).manual_seed(lowercase_)
   _UpperCamelCase           =    {
       "batch_size": 1,
       "generator": generator,
       "num_inference_steps": 2,
       "output_type": "numpy",
   }
   return inputs







 def 		__UpperCAmelCase				(		self				:   Dict)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    "cpu"

   _UpperCamelCase           =    self.get_dummy_components()
   _UpperCamelCase           =    self.pipeline_class(**lowercase_)
   pipe.to(lowercase_)
   pipe.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    self.get_dummy_inputs(lowercase_)
   _UpperCamelCase           =    pipe(**lowercase_).images
   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   self.assertEqual(image.shape							, (1, 32, 32, 3))
   _UpperCamelCase           =    np.array(
       [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4])
   _UpperCamelCase           =    np.abs(image_slice.flatten() - expected_slice).max()
   self.assertLessEqual(lowercase_							, 1e-3)







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Union[str, Any]:


   """simple docstring"""







   super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Union[str, Any]:


   """simple docstring"""







   super().test_save_load_local(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   super().test_save_load_optional_components(expected_max_difference=3e-3)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Dict:


   """simple docstring"""







   super().test_inference_batch_single_identical(expected_max_diff=3e-3)




@slow
@require_torch_gpu
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[Any])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    "google/ddpm-cifar10-32"

   _UpperCamelCase           =    UNetaDModel.from_pretrained(lowercase_)
   _UpperCamelCase           =    DDIMScheduler()

   _UpperCamelCase           =    DDIMPipeline(unet=lowercase_							, scheduler=lowercase_)
   ddim.to(lowercase_)
   ddim.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    ddim(generator=lowercase_							, eta=0.0							, output_type="numpy").images

   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   _UpperCamelCase           =    np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    "google/ddpm-ema-bedroom-256"

   _UpperCamelCase           =    UNetaDModel.from_pretrained(lowercase_)
   _UpperCamelCase           =    DDIMScheduler.from_pretrained(lowercase_)

   _UpperCamelCase           =    DDIMPipeline(unet=lowercase_							, scheduler=lowercase_)
   ddpm.to(lowercase_)
   ddpm.set_progress_bar_config(disable=lowercase_)

   _UpperCamelCase           =    torch.manual_seed(0)
   _UpperCamelCase           =    ddpm(generator=lowercase_							, output_type="numpy").images

   _UpperCamelCase           =    image[0, -3:, -3:, -1]

   assert image.shape == (1, 256, 256, 3)
   _UpperCamelCase           =    np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2




